docstr: Loads trained component from disk.
docstr_tok: ["Loads","trained","component","from","disk","."]
code: def load(
      cls,
      config: Dict[Text, Any],
      model_storage: ModelStorage,
      resource: Resource,
      execution_context: ExecutionContext,
  ) -> GraphComponent:
      """Loads trained component from disk."""
      try:
          with model_storage.read_from(resource) as model_dir:
              tfidfvectorizer = load(
                  model_dir / "tfidfvectorizer.joblib"
              )
              component = cls(
                  config,
                  execution_context.node_name,
                  model_storage,
                  resource,
              )
              component.tfm = tfidfvectorizer
      except (ValueError, FileNotFoundError):
          logger.debug(
              f"Couldn't load metadata for component '{cls.__name__}' as the persisted "
              f"model data couldn't be loaded."
          )
      return component
  
imports: import logging
  from typing import Any, Text, Dict, List, Type
  from sklearn.feature_extraction.text import TfidfVectorizer
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.sparse_featurizer.sparse_featurizer import SparseFeaturizer
  from rasa.nlu.tokenizers.tokenizer import Tokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import DENSE_FEATURIZABLE_ATTRIBUTES, FEATURIZER_CLASS_ALIAS
  from joblib import dump, load
  from rasa.shared.nlu.constants import TEXT, TEXT_TOKENS, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
label_desc: Use image node_id 11 for calling the TfIdfFeaturizer obj's underlying member method code with example usage: obj.load(cls, config, model_storage, resource, execution_context) and returns: component